Chronic low exposures to airborne particulates are widespread and the health implications are poorly understood. Existing statistical methods not previuosly applied to environmental epidemiology will be adapted for use in evaluating the respiratory effects of chronic low level exposures. 1) An autoregressive model is proposed for describing the behavior of longitudinal pulmonary function data as an alternative to the standard linear model. The autoregressive model predicts successive changes in lung function rather than a uniform rate of loss, and is therefore able to evaluate the effects of changes in exposure or smoking habits which occur during the study period. 2) The measurement errot inherent in both exposure and pulmonary function will be explicitly incorporated into the covariance structure of this model. 3) The variability in personal exposures will be examined by analysis of variance, and models for transforming cumulative exposures into estimates of dose will be proposed and evaluated. 4) The investigator will also explore the potential biases in prospective lung funtion data created by observations which are due to inadequate measurement, erratic participation, and loss to follow-up. Two unique sets of data on low environmenal exposures and lung function are available for this research project: the Six Cities air pollution study which includes the largest data set ever collected on longitudinal pulmonary function and the Vermont granite industry data on chronic low silica exposures.